How to compare saturated model to baseline model?

To test how well our model compares to a saturated model, we compute chi-square as follows, minus two times the differences in the log likelihoods; -2* (-2949.3343 – -2943.2087) = 12.2512. The degrees of freedom for this chi-square is the difference in the number of parameters estimated in the two model (20 – 15 = 5).
Accordingly, how well does your model compare to a saturated model?
To test how well our model compares to a saturated model, we compute chi-square as follows, minus two times the differences in the log likelihoods; -2* (-2949.3343 – -2943.2087) = 12.2512. The degrees of freedom for this chi-square is the difference in the number of parameters estimated in the two model (20 – 15 = 5).
Consequently, what is the baseline model? The baseline model. So, that brings us to the baseline model. This is defined in the Stata [SEM] Structural Equation Modeling Reference Manual as a model which includes the means and variances of all observed variables plus the covariances of all observed exogenous variables.
Furthermore, how many covariances are there in the baseline model?
Since there is only one observed exogenous variable, female, in our model, there will be no covariances in our baseline model. For the baseline model we estimated 10 parameters; 5 variances and 5 means. In comparing this model with the saturated model there was a difference of 10 degrees of freedom, 20 – 10 = 10.
Keeping this in consideration, how do you find the number of parameters in a saturated model?
You can compute the number of parameters in a saturated model of k observed variables by the formula k* (k+1)/2 + k. In our example, it is 5* (5+1)/2 + 5 = 20. The log likelihood for this model is -2943.2087. To test how well our model compares to a saturated model, we compute chi-square as follows,...

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